code: def _tile_dataframe(cls, op: "DataFrameResetIndex"):
      in_df = op.inputs[0]
      out_df = op.outputs[0]
      added_columns_num = len(out_df.dtypes) - len(in_df.dtypes)
      out_chunks = []
      index_has_value = out_df.index_value.has_value()
      chunk_has_nan = any(np.isnan(s) for s in in_df.nsplits[0])
      cum_range = np.cumsum((0,) + in_df.nsplits[0])
      for c in in_df.chunks:
          if index_has_value:
              if chunk_has_nan:
                  index_value = parse_index(pd.RangeIndex(-1))
              else:
                  index_value = parse_index(
                      pd.RangeIndex(
                          cum_range[c.index[0]],
                          cum_range[c.index[0] + 1],
                      )
                  )
          else:
              index_value = out_df.index_value
          if c.index[1] == 0:
              chunk_op = op.copy().reset_key()
              dtypes = out_df.dtypes[
                  : (added_columns_num + len(c.dtypes))
              ]
              columns_value = parse_index(dtypes.index)
              new_chunk = chunk_op.new_chunk(
                  [c],
                  shape=(c.shape[0], c.shape[1] + added_columns_num),
                  index=c.index,
                  index_value=index_value,
                  columns_value=columns_value,
                  dtypes=dtypes,
              )
          else:
              chunk_op = op.copy().reset_key()
              chunk_op._drop = True
              new_chunk = chunk_op.new_chunk(
                  [c],
                  shape=c.shape,
                  index_value=index_value,
                  index=c.index,
                  columns_value=c.columns_value,
                  dtypes=c.dtypes,
              )
          out_chunks.append(new_chunk)
      if not index_has_value or chunk_has_nan:
          if (
              isinstance(
                  out_df.index_value.value, IndexValue.RangeIndex
              )
              and op.incremental_index
          ):
              yield out_chunks
              out_chunks = standardize_range_index(out_chunks)
      new_op = op.copy()
      columns_splits = list(in_df.nsplits[1])
      columns_splits[0] += added_columns_num
      nsplits = calc_nsplits({c.index: c.shape for c in out_chunks})
      return new_op.new_dataframes(
          op.inputs,
          out_df.shape,
          nsplits=nsplits,
          chunks=out_chunks,
          dtypes=out_df.dtypes,
          index_value=out_df.index_value,
          columns_value=out_df.columns_value,
      )
  
imports: import pandas
  import numpy
  from ...None import opcodes
  from ...core import OutputType
  from ...serialization.serializables import AnyField, BoolField
  from ...utils import no_default, calc_nsplits
  from ..core import IndexValue
  from ..operands import DataFrameOperandMixin, DataFrameOperand, DATAFRAME_TYPE
  from ..utils import parse_index, build_empty_df, build_empty_series, standardize_range_index
label_desc: Use image node_id 9 for calling the DataFrameResetIndex obj's underlying member method code with example usage: obj._tile_dataframe(cls, op) and returns: new_op